We also formulate a new MVS framework, CDS-MVSNet, which aims at improving the quality of MVS reconstruction while decreasing computation time and memory consumption. Similar to previous works [1]}, [2]}, [3]}, [4]}, CDS-MVSNet is composed of a cascade network structure to estimate high-resolution depth maps in a coarse-to-fine manner. For each cascade stage, it formulates a 3D cost volume based on the output features of CDSFNet, which reduces the matching ambiguity by using proper pixel’s scale. For instance, consider a 3D point on the scene that needs to be reconstructed, CDSFNet can capture the same context information in both reference and source views for this point to extract the matchable features. Furthermore, CDS-MVSNet applies visibility-based cost aggregation to improve the performance of stereo matching. The pixel-wise visibility is estimated from the curvature information that encodes the matching ability of extracted features implicitly. Therefore, CDS-MVSNet performs accurate stereo matching and cost aggregation even on low-resolution images. Finally, the proposed network can generate high quality 3D models only by processing half-resolution images. This approach can significantly reduce the computation time and memory consumption of MVS reconstruction.
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